Field of Invention
The present invention relates to a video quality assessment method, and more particularly to a video quality objective assessment method based on a spatiotemporal domain structure.
Description of Related Arts
Video quality assessment (VQA for short) plays a key role in the rapid development of video-related applications, which is commonly used in quality control of a wide variety of video services and performance comparison of various video processing algorithms. Video quality objective assessment focuses on estimating the video quality by algorithms, so as to approximate the video quality perceived by human visual system. According to availability of original video without distortion, video quality objective assessment method is generally divided into three categories: full-reference (FR), reduced-reference and no-reference video quality assessment methods. Due to a higher accuracy, the full reference video quality assessment method (FR-VQA for short) is widely used in perceptual video processing such as encoding module selection, parameter quantization, rate control, and error concealment in video compression.
Full-reference video quality assessment method requires the ability to quickly and accurately evaluate the video objective quality, so real-time processing capability is very important, such as online source video quality monitoring and distortion metrics of rate-distortion optimized video encoder. Besides, in other applications, a low complexity is also a very important performance requirement for the full reference video quality assessment method. Pixel-level peak signal-to-noise ratio (PSNR for short) in the full-reference assessment method is conventionally the most widely used performance quantized indicator in video processing. Due to advantages such as convenient implement, fast evaluation, stable performance, and clear physical meaning, the peak signal-to-noise ratio is still the most widely used objective assessment method for most widely used video coding standard H.264/AVC and the latest H.265/HEVC. However, distortion of each pixel is treated equally without considering perceptual characteristics of the human visual system, which causes a low consistency between the peak signal-to-noise ratio and the subjective quality perception, thus impeding the progress of video processing technology, especially the progress of the video compression efficiency.
Conventional full-reference video quality assessment methods are divided into two categories. The first one, which is the most direct video quality assessment method, is to use effective image quality assessment method on independent frame, and then use average or weighted average for obtaining the video quality. However, such video quality assessment method lacks temporal information and assessment effect is poor. The second one respectively investigates spatial and temporal domains distortion for obtaining the video quality, or directly evaluates according to the spatial domain information. Although assessment effect of such method is better than that of the peak signal-to-noise ratio, complexity thereof is high and some need very time-consuming motion estimation. Therefore, disadvantages, such as difficult implement, impossible real-time processing and difficult integration, hinder the wide application of such video quality assessment method.